The Dartmouth Conference of 1956 was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference included this assertion: "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it". The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research. At the conference Newell and Simon debuted the "Logic Theorist" and McCarthy persuaded the attendees to accept "Artificial Intelligence" as the name of the field. The 1956 Dartmouth conference was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI.

Sure. But I'd wager that one day the various AI threads – speech, translation, simulation, driving, navigation, robotics, military, games, data mining, vision, &c. – will come together (perhaps even suddenly come together) and we're in for a surprise.

Sure. But I'd wager that one day the various AI threads – speech, translation, simulation, driving, navigation, robotics, military, games, data mining, vision, &c. – will come together (perhaps even suddenly come together) and we're in for a surprise.

Judea Pearl opines that all of those things represent, to one degree or another, nothing more than curve fitting. It's only half the problem. It's the how without the why. True AI requires the why.

To Build Truly Intelligent Machines, Teach Them Cause and Effect

Judea Pearl, a pioneering figure in artificial intelligence, argues that AI has been stuck in a decades-long rut. His prescription for progress? machines to understand the question why.

I felt an apostate when I developed powerful tools for prediction and diagnosis knowing already that this is merely the tip of human intelligence. If we want machines to reason about interventions (“What if we ban cigarettes?”) and introspection (“What if I had finished high school?”), we must invoke causal models. Associations are not enough — and this is a mathematical fact, not opinion.

In 2015, a black software developer embarrassed Google by tweeting that the company’s Photos service had labeled photos of him with a black friend as “gorillas.” Google declared itself “appalled and genuinely sorry.” An engineer who became the public face of the clean-up operation said the label gorilla would no longer be applied to groups of images, and that Google was “working on longer-term fixes.”

More than two years later, one of those fixes is erasing gorillas, and some other primates, from the service’s lexicon. The awkward workaround illustrates the difficulties Google and other tech companies face in advancing image-recognition technology, which the companies hope to use in self-driving cars, personal assistants, and other products.

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Be that as it may, I just did a Google Image search on "gorilla" and yields apparently OK results.

Send nudes plz... for the purposes of training this machine-learning software

Artificially intelligent software is used more and more to automatically detect and ban nude images on social networks and similar sites. However, today's algorithms and models aren't perfect at clocking racy snaps, and a lot of content moderation still falls to humans.

Enter an alternative solution: use AI to magically draw bikinis on photos to, er, cover up a woman’s naughty bits. A group of researchers from the Pontifical Catholic University of Rio Grande do Sul, Brazil, have trained generative adversarial networks to perform this very act, and automatically censor nudity.